Search engines have become a popular and nearly indispensable tool as a query method for quickly finding facts and data about the myriad of topics that can be retrieved on both public and private computer networks globally. These search engines serve as a central location to locate objective data in documents, such as web pages or published papers, as well as various public and private data sources. These commercially available search engines typically also return related salient pieces of information about the topic under consideration, as well as a generic description of the topic itself. For example, a computer search for the celebrity “Justin Bieber” on either search engine http://www.google.com or http://www.bing.com will return not only facts and data about Mr. Bieber, but also recent news articles about him, photographs of him, playlists containing his published recordings, lists of movies that he starred in, and other information relating to him.
Conventional search engines have been surprisingly slow in adapting to and incorporating the rapid advances in social media posts that have become the fabric of today's society and a reflection of general public sentiments on hot topics. Although search engines return useful facts and data about the topic under consideration, they suffer from drawbacks and do not return any of the following: human opinion about the topic under consideration; how much popular “buzz” exists—the total number of results returned, segregated by positive, negative, and neutral sentiment expressed about the topic under consideration; positivity, as expressed by favorable human sentiment, towards the topic under consideration; negativity, as expressed by unfavorable human sentiment, towards the topic under consideration; how public opinion, both positive and negative, about the topic under consideration has changed over time; and user feedback, including the ability for users to “vote up” or “vote down” a given search result.
In parallel with developments in search engine technology, there have been numerous conventional developments in sentiment analysis pertaining to natural language processing methods and software that can identify positive or negative human sentiment in a given sample of text. Various well-known methods exist for deriving such information, such as traditional polling, online survey tools, automated phone calls to survey recipients, etc., as well as numerous commercial and open source software packages that can be applied to measure and score the human sentiment contained in written text, speech, and other embodiments of natural language.
Prior sentiment analysis techniques possess disadvantages, which include missing several useful features. These techniques do not apply to the presentation of online advertisements. Current online advertisements do not incorporate human sentiment as a measure of ad relevance or context.
Accordingly, it is desirable to have a system and method that provide an opinion search platform that sources, analyzes, and computes large amounts of unstructured and structured social media electronic messages from various sources, featuring natural language processing with sentiment analysis and entity groupings, to produce one or more visual representations to reflect the opinion search result.